Status Page Scraper

Status Page Scraper provides a clean, visual dashboard that displays daily and periodic counts from your datasets in interactive charts. It helps teams monitor recurring data jobs, spot trends over time, and quickly verify that automated processes are running as expected.

     

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Introduction

This project creates a lightweight status dashboard that visualizes dataset growth across multiple time intervals. It solves the problem of manually checking raw numbers by turning them into clear charts and tables, making ongoing monitoring simple and reliable. It is ideal for developers, data teams, and operations engineers who run periodic data pipelines and want instant visibility into results.

Monitoring & Visualization Dashboard

• Runs a built-in web server to present real-time charts
• Supports multiple time intervals such as daily, weekly, and monthly views
• Displays multiple charts on a single dashboard
• Optionally renders tables with per-source daily counts
• Designed for long-running, recurring data workflows

Features

Feature	Description
Built-in Web Dashboard	Launches a web interface that visualizes dataset statistics in real time.
Flexible Time Intervals	View data by day, week, bi-weekly, monthly, or custom ranges.
Multiple Charts Support	Configure multiple charts with unique identifiers on one page.
Optional Data Tables	Show detailed tables below charts for daily breakdowns.
Persistent Dashboard Links	Supports stable URLs for sharing and bookmarking dashboards.
Manual & Automated Data Ingestion	Store dataset statistics either programmatically or via event hooks.

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What Data This Scraper Extracts

Field Name	Field Description
chartId	Unique identifier that links data points to a specific chart.
name	Human-readable name of the data source or process.
datasetId	Identifier of the dataset being monitored.
interval	Time window used for aggregation (day, week, month, etc.).
count	Number of records collected in the dataset for the interval.
color	Optional display color used for visualizing the chart line.

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Example Output

[
  {
    "chartId": "my-chart-1",
    "name": "daily-products",
    "datasetId": "ds_abc123",
    "interval": "day",
    "count": 1420,
    "color": "#0084d5"
  },
  {
    "chartId": "my-chart-1",
    "name": "daily-products",
    "datasetId": "ds_abc123",
    "interval": "week",
    "count": 9810,
    "color": "#0084d5"
  }
]

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Directory Structure Tree

status-page-scraper
├── src/
│   ├── server.js
│   ├── dashboard/
│   │   ├── charts.js
│   │   ├── tables.js
│   │   └── layout.js
│   ├── storage/
│   │   ├── storeData.js
│   │   └── readData.js
│   └── config/
│       └── settings.example.json
├── data/
│   └── samples.json
├── package.json
└── README.md

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Use Cases

• Data engineers use it to monitor daily dataset growth, so they can quickly detect pipeline failures.
• Operations teams rely on it to track recurring jobs, ensuring consistent delivery over time.
• Product teams visualize usage trends, helping them understand data volume patterns.
• Developers integrate it into internal tools to centralize monitoring across multiple processes.
• Analysts review historical counts to validate reporting accuracy.

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FAQs

How do I start the dashboard server? You can launch the server by running it in serve mode with a configuration that defines intervals and charts. Once started, it exposes a web URL where the dashboard is accessible.

Can I monitor more than one dataset at a time? Yes. The dashboard supports multiple charts, each with its own identifier, allowing you to track several datasets simultaneously.

Is it possible to update data automatically? Yes. Data can be stored automatically through event-based triggers or manually by calling the storage task directly from your workflow.

Can I customize how the charts look? You can define chart names, identifiers, and optional colors to visually distinguish different data sources.

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Performance Benchmarks and Results

Primary Metric: Processes and visualizes daily dataset updates with near-instant chart refresh after ingestion.

Reliability Metric: Designed for continuous operation with stable uptime during long-running monitoring sessions.

Efficiency Metric: Lightweight server footprint with minimal resource usage, suitable for always-on dashboards.

Quality Metric: Consistently accurate aggregation of dataset counts across all supported time intervals.

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